Hybrid Data-Driven and Multisequence Feature Fusion Fault Diagnosis Method for Electro-Hydrostatic Actuators of Transport Airplane

High-accuracy fault diagnosis is a crucial way to improve the reliability of electro-hydrostatic actuator (EHA) in transport airplane. Due to the limitation of aircraft structure, it is extremely difficult to obtain EHA fault data and to accurately assess the type of fault occurrence, so a process methodology is proposed for test signal excitation in an EHA simulation environment to obtain multisource fault data. Based on two EHA fault dataset, a multisequence fusion network (MSFN) is proposed for end-to-end fault diagnosis. MSFN has the advantages of light weight, high efficiency, and the ease of expansion, utilizes multiscale wide kernel convolutional neural network (CNN), deep dilated CNN, and long short-term memory network for parallel feature extraction, and the improved fusion channel and spatial attention mechanism for cross-fusion of different sequence features. Experimental results show that MSFN has high prediction accuracy and robustness under different intensity noise, achieving 98.56% average accuracy when SNR = 20, can effectively realize the rapid fault diagnosis of EHA under multiple complex working conditions.